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469
Regression Shrinkage and Selection Via the Lasso
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1994
"... We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactl ..."
Abstract

Cited by 4212 (49 self)
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that are exactly zero and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also
Regression Shrinkage and Selection via the
, 2003
"... We propose the elastic net, a new regression shrinkage and selection method. Real data and a simulation study show that the elastic net often outperforms the lasso, while it enjoys a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strong corre ..."
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We propose the elastic net, a new regression shrinkage and selection method. Real data and a simulation study show that the elastic net often outperforms the lasso, while it enjoys a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strong
Simultaneous regression shrinkage, variable selection and clustering of predictors with
 OSCAR, Biometrics
, 2007
"... Summary. Variable selection can be challenging, particularly in situations with a large number of predictors with possibly high correlations, such as gene expression data. In this paper, a new method called the OSCAR (Octagonal Shrinkage and Clustering Algorithm for Regression) is proposed to simult ..."
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Cited by 87 (7 self)
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Summary. Variable selection can be challenging, particularly in situations with a large number of predictors with possibly high correlations, such as gene expression data. In this paper, a new method called the OSCAR (Octagonal Shrinkage and Clustering Algorithm for Regression) is proposed
On Mixture Regression Shrinkage and Selection via the MRLASSO
"... In finite mixture regression models, we generalize the application of the least absolute shrinkage and selection operator (LASSO) to obtain MRLasso, which incorporates both mixture and regression penalties. Because MRLasso jointly penalizes both regression coefficients and mixture components, it e ..."
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In finite mixture regression models, we generalize the application of the least absolute shrinkage and selection operator (LASSO) to obtain MRLasso, which incorporates both mixture and regression penalties. Because MRLasso jointly penalizes both regression coefficients and mixture components
Institute of Statistics Mimeo Series No. 2583 Simultaneous regression shrinkage, variable selection and clustering of predictors with OSCAR
"... Simultaneous regression shrinkage, variable selection and clustering of predictors with OSCAR ..."
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Simultaneous regression shrinkage, variable selection and clustering of predictors with OSCAR
Robust regression shrinkage and consistent variable selection via the LADLASSO
 JOURNAL OF BUSINESS AND ECONOMIC STATISTICS
, 2005
"... The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection. In this article we combine these two classical ideas together to produce LADla ..."
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Cited by 58 (7 self)
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The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection. In this article we combine these two classical ideas together to produce LAD
QuasiRegression With Shrinkage
 MATH. COMPUT. SIMUL
, 2003
"... Quasiregression is a method of Monte Carlo approximation useful for global sensitivity analysis. This paper presents a new version, incorporating shrinkage parameters of the type used in wavelet approximation. As an example application, a black box function from machine learning is analyzed. That f ..."
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Cited by 6 (2 self)
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Quasiregression is a method of Monte Carlo approximation useful for global sensitivity analysis. This paper presents a new version, incorporating shrinkage parameters of the type used in wavelet approximation. As an example application, a black box function from machine learning is analyzed
On the LASSO and Its Dual
 Journal of Computational and Graphical Statistics
, 1999
"... Proposed by Tibshirani (1996), the LASSO (least absolute shrinkage and selection operator) estimates a vector of regression coe#cients by minimising the residual sum of squares subject to a constraint on the l 1 norm of coe#cient vector. The LASSO estimator typically has one or more zero elements ..."
Abstract

Cited by 209 (2 self)
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Proposed by Tibshirani (1996), the LASSO (least absolute shrinkage and selection operator) estimates a vector of regression coe#cients by minimising the residual sum of squares subject to a constraint on the l 1 norm of coe#cient vector. The LASSO estimator typically has one or more zero
Shrinkage structure in biased regression
"... Biased regression is an alternative to ordinary least squares (OLS) regression, especially when explanatory variables are highly correlated. In this paper, we examine the geometrical structure of the shrinkage factors of biased estimators. We show that, in most cases, shrinkage factors cannot belon ..."
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Biased regression is an alternative to ordinary least squares (OLS) regression, especially when explanatory variables are highly correlated. In this paper, we examine the geometrical structure of the shrinkage factors of biased estimators. We show that, in most cases, shrinkage factors cannot
Results 1  10
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469